1. POR vs EXP color in trend charts:
   • add col for POR or exp
   • group by por and exp, then plot each group on same figure
2. median value trend charts for POR -
   • new df with median values by por and exp
3. add yline for solution change, target change, POR change, etc
4. plot by exp # instead of date
5. filter by meas no eff
6. fulltrends df with historical process, ivt, exp, xrf, etc

from IPython.display import Javascript, display
from ipywidgets import widgets

def run_all(ev):
    display(Javascript('IPython.notebook.execute_cells_below()'))

button = widgets.Button(description="Click here to update tables + charts:")
button.on_click(run_all)
display(button)

import pandas as pd
import numpy as np
import sqlalchemy as sa
import glob
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import plotly.plotly as py
from plotly.graph_objs import *
import datetime
%matplotlib inline  
#%pylab inline
pylab.rcParams['figure.figsize'] = (15, 25)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import qgrid
qgrid.nbinstall(overwrite=True) 

engine = sa.create_engine('mysql+pymysql://root:@localhost/rsi?charset=utf8')
conn = engine.connect()

update tables and send to db

process log

#updates process table in db and creates process df
df = pd.read_excel("//SERVER01/Public/ProcessingData_6in_R&DLine.xlsx", header=0)
df.to_sql('process', conn, if_exists='replace', index = True, index_label='process_index')

#del processdf
processdf = df
#qgrid.show_grid(processdf)

#update once a week
process_df_list = []
p = "//SERVER01/Public/Processing Data and Experiment Logs Archives and Backup/ProcessingData_6in_R&DLine_*.xlsx"
processFiles = glob.glob(p)

for i,f in enumerate(processFiles):
    sys.stdout.write('\r')
    sys.stdout.write(str(i))
    sys.stdout.flush()
    
    df_1 = pd.read_excel(f, header = 0)
    process_df_list.append(df_1)

process_df_list = pd.concat(process_df_list)

oldp = pd.DataFrame(process_df_list)
oldp1 = oldp.reindex_axis(df.columns, axis=1)

#del processdf
fprocessdf = pd.concat([oldp1, df])
fprocessdf
fprocessdf.to_sql('fullprocess', conn, if_exists='replace', index = True, index_label='fullprocess_index')

11

experiment log

#creates expdf
df2 = pd.read_excel("//SERVER01/Public/ExperimentLog.xlsx", header=0, parse_cols=[0,1,2,3,4,5,6,8])

#df2.dropna(axis=1, how='all') #remove na col

#df2["Experiment_ID"] = df2["Experiment_ID"].str.replace("Exp_","").astype(float)
#df2["Experiment_ID"] = df2["Experiment_ID"].str.replace("EXP_","").astype(float)

df2['Experiment_ID'] = df2['Experiment_ID'].str.replace("Exp_","")
df2['Experiment_ID'] = df2['Experiment_ID'].str.replace("EXP_","")
df2['Experiment_ID'] = df2['Experiment_ID'].astype(float)

#del expdf
expdf = df2

#def mask(df, f):
#    return df[f(df)]
#def mask(df, key, value):
#    return df[df[key] == value]
#df.mask('A', 1).mask('D', 6)

#add POR or EXP classification
expdf['POR'] = 'EXP'
mask1 = expdf.Split1.str.contains('.*POR.*', na=False)
expdf.loc[:,'POR'][mask1] = 'POR'
#expdf.mask('POR',)
mask2 = expdf.Split2.str.contains('.*POR.*', na=False)
expdf.loc[:,'POR'][mask2] = 'POR'
mask3 = expdf.Split3.str.contains('.*POR.*', na=False)
expdf.loc[:,'POR'][mask3] = 'POR'
mask4 = expdf.Description.str.contains('.*POR.*', na=False)
expdf.loc[:,'POR'][mask4] = 'POR'

#updates exp log in db
expdf.to_sql('exp', conn, if_exists='replace', index = True, index_label='exp_index')
expdf.head()
#expdf.dtypes

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:25: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:28: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:30: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:32: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

	Sample ID	Experiment_ID	Description	Date (IVT measure)	Split1	Split2	Split3	Comments	POR
0	39H6F	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR
1	39H6G	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	Non diluted - 34%	NaN	NaN	NaN	EXP
2	39H7A	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	Non diluted - 34%	NaN	NaN	NaN	EXP
3	39H7B	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR
4	39H7C	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR

#push to sql once a wk
e = pd.read_excel("//SERVER01/Public/Processing Data and Experiment Logs Archives and Backup/ExperimentLog - Jun05.xlsx", header=0, parse_cols=[0,1,2,4,5,6,7])
e1 = e.reindex_axis(df2.columns, axis=1)
 
e1['Experiment_ID'] = e1['Experiment_ID'].str.replace("Exp_","")
e1['Experiment_ID'] = e1['Experiment_ID'].str.replace("EXP_","")
e1['Experiment_ID'] = e1['Experiment_ID'].str.replace("exp_","")
e1['Experiment_ID'] = e1['Experiment_ID'].astype(float)
                  
#add POR or EXP classification
e1['POR'] = 'EXP'
mask1 = e1.Split1.str.contains('.*POR.*', na=False)
e1.loc[:,'POR'][mask1] = 'POR'
mask2 = e1.Split2.str.contains('.*POR.*', na=False)
e1.loc[:,'POR'][mask2] = 'POR'
mask3 = e1.Split3.str.contains('.*POR.*', na=False)
e1.loc[:,'POR'][mask3] = 'POR'
mask4 = e1.Description.str.contains('.*POR.*', na=False)
e1.loc[:,'POR'][mask4] = 'POR'
                  
fullexpdf = pd.concat([e1, expdf])
fullexpdf.to_sql('fullexp', conn, if_exists='replace', index = True, index_label='fexp_index')
fullexpdf.head()

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:13: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:15: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:17: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:19: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

	Sample ID	Experiment_ID	Description	Date (IVT measure)	Split1	Split2	Split3	Comments	POR
0	32I59	1	Optimize CuI process to eliminate JL(V) and in...	NaN	CuI 0.250L/min (POR)	CuI Anneal 10 min. (POR)	CuI Anneal 170°C (POR)	NaN	POR
1	32J07	1	Optimize CuI process to eliminate JL(V) and in...	NaN	CuI 0.250L/min (POR)	CuI Anneal 10 min. (POR)	CuI Anneal 170°C (POR)	NaN	POR
2	32J19	1	Optimize CuI process to eliminate JL(V) and in...	NaN	CuI 0.250L/min (POR)	CuI Anneal 10 min. (POR)	CuI Anneal 170°C (POR)	NaN	POR
3	32I63	1	Optimize CuI process to eliminate JL(V) and in...	NaN	CuI 0.250L/min (POR)	CuI Anneal 15 min.	CuI Anneal 170°C (POR)	NaN	POR
4	32J10	1	Optimize CuI process to eliminate JL(V) and in...	NaN	CuI 0.250L/min (POR)	CuI Anneal 15 min.	CuI Anneal 170°C (POR)	NaN	POR

CdTe logs (inc)

#updates ED log table in db
#ED_data_list = []
#efiles = \\6in-cdte1\Users\reelsolar\Documents\CdTe Log Data\*.xls
#(time, soln temp, chuck temp(deprecated), flow rate, side + pin currents)
#e1files = \\6in-cdte1\Users\reelsolar\Documents\CdTe Log Data\*P.xls
#(time, soln temp, flow rate, pin currents)


#EDFiles = allFiles = glob.glob("//ED3-pc/Users/ReelSolar/Documents/CdTe Log Data/*.xls")
#for i,f in enumerate(allFiles):
#    sys.stdout.write('\r')
#    sys.stdout.write(str(i))
#    sys.stdout.flush()
    
#    df4 = pd.read_excel(f, sep='\t', header = 0, skiprow=1)
#    ED_data_list.append(df4)
    
#ED_data_list = pd.concat(ED_data_list)
#filename as index, 
#print('Done.')

#ED info
ed = pd.read_csv("//SERVER01/Public/SPC/Database/ED.csv", header=0, skip_blank_lines=True)
ed.to_sql('ed', conn, if_exists='replace', index = True, index_label='ed_index')
print('Done.')

Done.

#XRF info
xrf = pd.read_csv("//SERVER01/Public/SPC/Database/XRF.csv", header=0, skip_blank_lines=True)
xrf.to_sql('xrf', conn, if_exists='replace', index = True, index_label='xrf_index')
print('Done.')

Done.

Sencera log

#updates sencera log table in db and creates senceradf
p1 = "//SERVER01/Public/Process Modules/08 - Sputter/Sencera MoNAl Process Monitor.xlsx"
df5 = pd.read_excel(p1, header=0, parse_cols=[0,1,2,3,9,10,11,12,13,16,17,18,19,20,22,23,24,26,27])
df5.to_sql('sencera', conn, if_exists='replace', index = True, index_label='sencera_index')

#sencera = df5[df5['Date_tm'].apply(lambda x: type(x)==datetime.datetime)==True]
sencera = df5
sencera.tail()
print('Done.')

Done.

full (DIV + LIV) IVT fits

#updates IVT data to send to db

#----------append routine, only use if # of cols vary between ivt files. if not, concat -------#
#path =r"//rsi-ivt-pc/Users/Reel Solar/IV curves/" can use below instead of full path, path + ""
#df3 = pd.concat(pd.read_csv(f) for f in allFiles)
#len(allFiles) 10876 02/22/16
#IVT_data = pd.DataFrame()
#for i,f in enumerate(allFiles):
#    sys.stdout.write('\r')
#    sys.stdout.write(str(i))
#    sys.stdout.flush()
    
#    df3 = pd.read_csv(f, sep='\t', header = 0)
#    IVT_data = IVT_data.append(df3, ignore_index=True)


IVT_data_list = []
allFiles = glob.glob("//rsi-ivt-pc/Users/Reel Solar/IV curves/*fit_parameters.txt")
for i,f in enumerate(allFiles):
    sys.stdout.write('\r')
    sys.stdout.write(str(i))
    sys.stdout.flush()
    
    df3_2 = pd.read_csv(f, sep='\t', header = 0, parse_dates=['Date_tm'])
    IVT_data_list.append(df3_2)
    #IVT_data3 = pd.concat(IVT_data_list, ignore_index=True)
    
%time IVT_data_list = pd.concat(IVT_data_list)
print('Done.')

12666Wall time: 27 s
Done.

#modifies ivtdf, sets datetime info 
ivtdf = IVT_data_list.drop('n', 1) #remove null and inf cols
ivtdf = IVT_data_list.drop('IO_nA', 1)
ivtdf = ivtdf.drop('fit_check', 1)
ivtdf = ivtdf.drop('fit_res_var',1)

#panda i/o to sql has problems with inf values, routine below replaces these with nan ==> None in SQL:
#ivtdf = ivtdf.where(pd.notnull(ivtdf), None)
ivtdf = ivtdf.replace([np.inf, -np.inf], np.nan)

ivtdf = ivtdf[ivtdf['Date_tm'].apply(lambda x: type(x)==datetime.datetime)==True]
ivtdf = ivtdf[~ivtdf['Sample_ID'].str.contains('.*PHILIP.*', na = False)]
ivtdf['Rsh_Ohm'] = ivtdf['Rsh_Ohm'].astype(float)
#ivtdf['Rsh'] = ivtdf['Rsh_Ohm']/ivtdf['Cell_Count'] fix gives inf

#def shunt(row):
#    if row['Rsh'] <=50.0:
#        return 1
#ivtdf['shunt_count'] = ivtdf.apply(lambda row: shunt(row), axis=1)

ivtdf.info() #confirm removal/ replacement

<class 'pandas.core.frame.DataFrame'>
Int64Index: 684902 entries, 0 to 143
Data columns (total 28 columns):
Cell_Area_mm2    684901 non-null float64
Cell_Count       684081 non-null float64
Cell_no          684902 non-null float64
Comment          142425 non-null object
Current_Step     684902 non-null object
Date_tm          684902 non-null object
Eff              821 non-null float64
Efficiency       344486 non-null float64
FF               821 non-null float64
FillFactor       346255 non-null float64
I0_nA            293984 non-null object
Iph_mA           294151 non-null object
Isc_mA           347801 non-null float64
Measurement      684902 non-null object
NumCells         821 non-null float64
Pmp_fit_ratio    150444 non-null float64
Pmp_mW           347721 non-null float64
R_squared        290509 non-null object
Roc_Ohm          347611 non-null float64
Rs_Ohm           672104 non-null float64
Rsc_Ohm          347607 non-null float64
Rsh_Ohm          672372 non-null float64
Sample_ID        684902 non-null object
Temp_C           684901 non-null float64
User             684476 non-null object
Vmp_V            347801 non-null float64
Voc_V            347140 non-null float64
n                294019 non-null float64
dtypes: float64(19), object(9)
memory usage: 151.5+ MB

#sends ivt data to sql db
%time ivtdf.to_sql('ivt', conn, if_exists='replace', index = True, index_label='ivt_index')
print('Done.')

Wall time: 1min 57s
Done.

creates module and cell trends dataframes

#creates ivtdfmod and module trends dfs
ivtdfmod = pd.DataFrame(ivtdf[ivtdf.Cell_Count > 19])
#list(ivtdfmod.columns.values)

#del mod_trends
ivtdfmod['Rsh_Ohm'] = ivtdfmod['Rsh_Ohm'].astype(float)
mod_trends = pd.merge(left=expdf, right=ivtdfmod, left_on='Sample ID', right_on='Sample_ID')
mod_trends.sort_values(by='Experiment_ID')
mod_trends = mod_trends.replace([np.inf, -np.inf], np.nan)

mod_trends['percentEff'] = mod_trends['Efficiency']*100
mod_trends['percentFF'] = mod_trends['FillFactor']*100
mod_trends['modVoc'] = mod_trends['Voc_V']/mod_trends['Cell_Count']
mod_trends['modJsc'] = (mod_trends['Isc_mA']/mod_trends['Cell_Area_mm2'])*100
mod_trends['modRs'] = mod_trends['Roc_Ohm']/mod_trends['Cell_Count']
mod_trends['modRsh'] = mod_trends['Rsc_Ohm']/mod_trends['Cell_Count']

def mod_shunt(row):
    if row['modRsh'] <=50.:
        return 1
    
mod_trends['shunt_count'] = mod_trends.apply(lambda row: mod_shunt(row), axis=1)
#mod_trends.sort_values(by='Date (IVT Measure)')

mod_trends = mod_trends.replace([np.inf, -np.inf], np.nan)
mod_trends.head() #confirms accurate mod measurements

	Sample ID	Experiment_ID	Description	Date (IVT measure)	Split1	Split2	Split3	Comments	POR	Cell_Area_mm2	Cell_Count	Cell_no	Comment	Current_Step	Date_tm	Eff	Efficiency	FF	FillFactor	I0_nA	Iph_mA	Isc_mA	Measurement	NumCells	Pmp_fit_ratio	Pmp_mW	R_squared	Roc_Ohm	Rs_Ohm	Rsc_Ohm	Rsh_Ohm	Sample_ID	Temp_C	User	Vmp_V	Voc_V	n	percentEff	percentFF	modVoc	modJsc	modRs	modRsh	shunt_count
0	39H6F	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR	6.273	23	1	NaN	IV_TEST	2014-10-03 16:43:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Dark	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	39H6F	27.298789	HP	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	39H6F	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR	6.273	23	1	NaN	IV_TEST	2014-10-03 16:44:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Dark	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	39H6F	28.756583	HP	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	39H6F	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR	627.300	23	1	NaN	IV_TEST	2014-10-03 16:44:00	NaN	0.099741	NaN	0.542762	NaN	NaN	154.005353	Light	NaN	NaN	1439.054850	NaN	27.322773	NaN	1559.617300	NaN	39H6F	28.834435	HP	12.232767	17.215998	NaN	9.9741	54.2762	0.748522	24.550511	1.187947	67.809448	NaN
3	39H6F	256	AQ5000 Photoresist 27% vs. 34% solids	2014-06-13 00:00:00	POR - 27%	NaN	NaN	NaN	POR	627.300	23	1	NaN	IV_TEST	2014-10-03 16:45:00	NaN	0.099454	NaN	0.559052	NaN	NaN	151.846239	Light	NaN	NaN	1434.911937	NaN	25.811509	NaN	22421.341523	NaN	39H6F	31.149857	HP	11.872947	16.903206	NaN	9.9454	55.9052	0.734922	24.206319	1.122240	974.840936	NaN
4	39H6F	305	Enki AR Coating post CdTe	2014-10-03 00:00:00	AR Coat post PR	FoilAnneal	NaN	NaN	EXP	6.273	23	1	NaN	IV_TEST	2014-10-03 16:43:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Dark	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	39H6F	27.298789	HP	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

#creates cell trends df

#del cell_trends
ivtdfcell = pd.DataFrame(ivtdf[ivtdf.Cell_Count == 1 ])

ivtdfcell['Rsh_Ohm'] = ivtdfcell['Rsh_Ohm'].astype(float)
cell_trends = pd.merge(left=expdf, right=ivtdfcell, left_on='Sample ID', right_on='Sample_ID')
#trends.sort_values(by='POR')
cell_trends.sort_values(by='Date_tm')
#trends.tail(50)

cell_trends['percentEff'] = cell_trends['Efficiency']*100
cell_trends['percentFF']= cell_trends['FillFactor']*100
cell_trends['cellVoc'] = cell_trends['Voc_V']/cell_trends['Cell_Count']
cell_trends['cellJsc']= (cell_trends['Isc_mA']/cell_trends['Cell_Area_mm2'])*100
cell_trends['cellRs'] = cell_trends['Rs_Ohm']/cell_trends['Cell_Count']
cell_trends['cellRs_norm'] = (cell_trends['Rs_Ohm']/cell_trends['Cell_Count'])*(cell_trends['Cell_Area_mm2']/100)
cell_trends['cellRsh'] = cell_trends['Rsc_Ohm']/cell_trends['Cell_Count']
cell_trends['cellRsh_norm'] = (cell_trends['Rsc_Ohm']/cell_trends['Cell_Count'])*(cell_trends['Cell_Area_mm2']/100)

#for i in len(cell_trends['cellRs']):
#    if mask1 = expdf.Split1.str.contains('.*POR.*', na=False):
#        expdf.loc[:,'POR'][mask1] = 'POR'
#    else:

def cell_shunt(row):
    if row['cellRsh'] <=50.:
        return 1
    
cell_trends['shunt_count'] = cell_trends.apply(lambda row: cell_shunt(row), axis=1)
cell_trends = cell_trends.replace([np.inf, -np.inf], np.nan)
cell_trends.tail()
#list(cell_trends.columns.values)

	Sample ID	Experiment_ID	Description	Date (IVT measure)	Split1	Split2	Split3	Comments	POR	Cell_Area_mm2	Cell_Count	Cell_no	Comment	Current_Step	Date_tm	Eff	Efficiency	FF	FillFactor	I0_nA	Iph_mA	Isc_mA	Measurement	NumCells	Pmp_fit_ratio	Pmp_mW	R_squared	Roc_Ohm	Rs_Ohm	Rsc_Ohm	Rsh_Ohm	Sample_ID	Temp_C	User	Vmp_V	Voc_V	n	percentEff	percentFF	cellVoc	cellJsc	cellRs	cellRs_norm	cellRsh	cellRsh_norm	shunt_count
374011	48Y1D	674	CdS pressure	2016-06-06 00:00:00	Materion CdS, 10 mT (36 sccm , POR)	8 samples in Cu anneal	tool2-1	NaN	POR	565	1	12	NaN	IV_TEST	2016-06-06 18:47:00	NaN	0.151980	NaN	0.715713	NaN	NaN	146.909187	Light	NaN	NaN	85.868964	NaN	0.883903	0.517827	102.067372	176.866021	48Y1D	26.923976	AH	0.675220	0.816674	NaN	15.1980	71.5713	0.816674	26.001626	0.517827	2.925723	102.067372	576.680652	NaN
374012	48Y1D	674	CdS pressure	2016-06-06 00:00:00	Materion CdS, 10 mT (36 sccm , POR)	8 samples in Cu anneal	tool2-1	NaN	POR	565	1	18	NaN	IV_TEST	2016-06-06 18:47:00	NaN	0.151579	NaN	0.716206	NaN	NaN	146.345447	Light	NaN	NaN	85.642130	NaN	0.884275	0.514661	298.063684	105.647828	48Y1D	27.080042	AH	0.648010	0.817091	NaN	15.1579	71.6206	0.817091	25.901849	0.514661	2.907835	298.063684	1684.059815	NaN
374013	48Y1D	674	CdS pressure	2016-06-06 00:00:00	Materion CdS, 10 mT (36 sccm , POR)	8 samples in Cu anneal	tool2-1	NaN	POR	565	1	11	NaN	IV_TEST	2016-06-06 18:47:00	NaN	0.153082	NaN	0.713647	NaN	NaN	148.207942	Light	NaN	NaN	86.491431	NaN	0.825139	0.505157	398.758024	119.365737	48Y1D	27.231372	AH	0.662041	0.817746	NaN	15.3082	71.3647	0.817746	26.231494	0.505157	2.854137	398.758024	2252.982836	NaN
374014	48Y1D	674	CdS pressure	2016-06-06 00:00:00	Materion CdS, 10 mT (36 sccm , POR)	8 samples in Cu anneal	tool2-1	NaN	POR	565	1	23	NaN	IV_TEST	2016-06-06 18:47:00	NaN	0.000000	NaN	NaN	NaN	NaN	0.000000	Light	NaN	NaN	0.000000	NaN	0.056516	0.171614	0.119464	0.129117	48Y1D	27.397998	AH	0.000000	NaN	NaN	0.0000	NaN	NaN	0.000000	0.171614	0.969619	0.119464	0.674972	1
374015	48Y1D	674	CdS pressure	2016-06-06 00:00:00	Materion CdS, 10 mT (36 sccm , POR)	8 samples in Cu anneal	tool2-1	NaN	POR	565	1	16	NaN	IV_TEST	2016-06-06 18:47:00	NaN	0.151351	NaN	0.709976	NaN	NaN	147.376047	Light	NaN	NaN	85.513353	NaN	0.829064	0.505941	36.876274	99.723587	48Y1D	27.540093	AH	0.639592	0.817266	NaN	15.1351	70.9976	0.817266	26.084256	0.505941	2.858567	36.876274	208.350948	1

#creates trends df = ivtdf + expdf + processdf

#del trends
trends = pd.merge(pd.merge(expdf, processdf), ivtdf, left_on='Sample ID', right_on='Sample_ID')

trends['percentEff'] = trends['Efficiency']*100
trends['percentFF'] = trends['FillFactor']*100
trends['Voc'] = trends['Voc_V']/trends['Cell_Count']
trends['Jsc'] = (trends['Isc_mA']/trends['Cell_Area_mm2'])*100
trends['Rs'] = trends['Roc_Ohm']/trends['Cell_Count']
trends['Rsh'] = trends['Rsc_Ohm']/trends['Cell_Count']

trends['CdS Thickness (nm)'][trends['CdS Thickness (nm)'] == ''] = None
trends['CdS Thickness (nm)'][trends['CdS Thickness (nm)'] == ' '] = None 
trends['CdS Thickness (nm)'][trends['CdS Thickness (nm)'] == '?'] = None 
trends['CdS Thickness (nm)'] = trends['CdS Thickness (nm)'].astype(float)
trends['CdTe Thickness in Angstroms'][trends['CdTe Thickness in Angstroms'] == ''] = None
trends['CdTe Thickness in Angstroms'][trends['CdTe Thickness in Angstroms'] == ' '] = None
trends['CdTe Thickness in Angstroms'] = trends['CdTe Thickness in Angstroms'].astype(float)

trends = trends.replace([np.inf, -np.inf], np.nan)

#trends.to_sql('trends', conn, if_exists='replace', index = True, index_label='trends_index')


#trends.tail()

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:13: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:14: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:15: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:17: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:18: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

#cds thickness trends

#del cds
#df = df.apply(lambda x: x.str.strip()).replace('', np.nan)
#cds = trends[pd.notnull(trends['CdS Thickness (nm)'])]
#cds = cds.apply(lambda x: x.str.strip()).replace('?', np.nan)

cds = pd.DataFrame(trends[trends.Cell_Count >= 22])
cds = cds[~cds['Measurement'].str.contains('.*Dark.*', na = False)]

cds = cds.replace([np.inf, -np.inf], np.nan)

cds = pd.merge(cds, xrf, left_on='Sample ID', right_on='Sample')

cds['CdTe_Tool'] = '1'
mask1 = cds["CdTe 2 Tool"].str.contains('.*TANK.*', na=False)
cds.loc[:,'CdTe_Tool'][mask1] = '2'


#cds.tail(20)

#from IPython.display import display
#grid = qgrid.QGridWidget(df=cds)
#display(grid)

qgrid.show_grid(cds, show_toolbar=True, grid_options={'forceFitColumns': False, 'defaultColumnWidth': 150})

c:\anaconda3\lib\site-packages\ipykernel\__main__.py:17: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

#particulate data
f = '//rsi-ivt-pc/Users/Reel Solar/Desktop//Particulate Analysis.xlsx'
p = pd.read_excel(f, 'Sheet1')
p.info()

p.to_sql('particle', conn, if_exists='replace', index = True, index_label='particle_index')

<class 'pandas.core.frame.DataFrame'>
Int64Index: 347148 entries, 0 to 347147
Data columns (total 4 columns):
DATE         347148 non-null datetime64[ns]
TIME         347148 non-null object
SMALL 0.5    347148 non-null int64
LARGE 2.5    347148 non-null int64
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 13.2+ MB

#SQL trend outs
cell_trends.to_sql('cells', conn, if_exists='replace', index = True, index_label='cells_index')
mod_trends.to_sql('mods', conn, if_exists='replace', index = True, index_label='mods_index')
trends.to_sql('trends', conn, if_exists='replace', index = True, index_label='trends_index')
cds.to_sql('cds', conn, if_exists='replace', index = True, index_label='cds_index')

import time 
now = time.strftime("%c")
print ("Updated at: %s"  % now )

Updated at: Tue Jun  7 08:44:49 2016

if you want to output to Excel:

#exports full trends df to excel
from xlsxwriter.utility import xl_rowcol_to_cell
writer_orig = pd.ExcelWriter('db.xlsx', engine='xlsxwriter')
%time cds.to_excel(writer_orig, index=False, sheet_name='all')
writer_orig.save()
print('Exported to db.xlsx.')

Wall time: 25 s
Exported to db.xlsx.

trend charts

from IPython.display import Javascript, display
from ipywidgets import widgets

def run_all(ev):
    display(Javascript('IPython.notebook.execute_cells_below()'))

button = widgets.Button(description="Click here to update tables + charts:")
button.on_click(run_all)
display(button)

##module eff, FF, Voc, Jsc vs time
plt.figure(2)
plt.figure(figsize=(20,10))
plt.subplot(211)
plt.plot(ivtdfmod['Date_tm'], ivtdfmod['Efficiency']*100, '+')
plt.xlim([datetime.date(2016, 1, 1), datetime.date(2016, 6, 15)])
plt.ylim(8, 17)
#plt.title("6\" progress -- Eff")
#plt.xlabel('date')
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.ylabel('Module Efficiency')
#plt.show()

plt.subplot(212)
plt.plot(ivtdfmod['Date_tm'], ivtdfmod['FillFactor']*100, 'g+')
plt.xlim([datetime.date(2016, 1, 1), datetime.date(2016,6,15)])
plt.ylim(55, 80)
#plt.title("6\" progress -- FF")
#plt.xlabel('date')
plt.ylabel('FF')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.show()

<matplotlib.figure.Figure at 0x20832813d30>

plt.figure(3)
plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(mod_trends['Date_tm'], mod_trends['modJsc'], 'r+')
plt.xlim([datetime.date(2016, 1, 1), datetime.date(2016,6,15)])
plt.ylim(20, 28)
#plt.title("6\" progress -- Jsc")
#plt.xlabel('date')
plt.ylabel('Jsc [mA/cm^2]')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
#plt.show()

plt.subplot(312)
plt.plot(mod_trends['Date_tm'], mod_trends['modVoc'], '+m')
plt.xlim([datetime.date(2016, 1, 1), datetime.date(2016,6,15)])
plt.ylim(0.78, .85)
#plt.title("6\" progress -- Voc")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.xlabel('date')
plt.ylabel('Voc [V]')
plt.show()

<matplotlib.figure.Figure at 0x208327ba940>

ivtdfmod['Rsh_Ohm'] = ivtdfmod['Rsh_Ohm'].astype(float)

#ivtdfmod['mean_Rsh'] = ivtdfmod['Rsh_Ohm'].mean
#ivtdfmod['mean_Rsh']

plt.figure(3)
plt.figure(figsize=(20,15))
plt.subplot(311)
plt.plot(ivtdfmod['Date_tm'], ivtdfmod['Rs_Ohm']/22, 'k+')
plt.xlim([datetime.date(2016,1, 1), datetime.date(2016,6,15)])
plt.ylim(0, 6)
#plt.title("6\" progress -- Eff")
#plt.xlabel('date')
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.ylabel('Rs/cell [Ohm]')
#plt.show()

plt.subplot(312)
plt.plot(ivtdfmod['Date_tm'], ivtdfmod['Rsh_Ohm']/22, 'y+')
plt.xlim([datetime.date(2016,1, 1), datetime.date(2016,6,15)])
plt.ylim(1e0, 1e8)
#plt.title("6\" progress -- Eff")
#plt.xlabel('date')
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.ylabel('Rsh/cell [Ohm]')
plt.yscale('log')
plt.show()

<matplotlib.figure.Figure at 0x208327cd5c0>

efficiency + FF

import seaborn as sns
flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("notebook", font_scale=1.5,)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

#_cond = ['POR', 'EXP']

#trends['POR'] = trends['POR'].apply(str)

fg = sns.FacetGrid(mod_trends, hue='POR', size= 9, aspect=2.3)#, hue_kws=dict(marker=["^", "v"]))
fg.map(plt.scatter, 'Experiment_ID', 'percentEff', alpha=.7,)
plt.xlim(560,680)
plt.ylim(12, 17)
plt.axhline(15.5, color='k', linestyle='--')
plt.axhline(14.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg1 = sns.FacetGrid(mod_trends, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'percentFF', alpha=.7)
plt.xlim(560,680)
plt.ylim(50, 80)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()


#g = sns.FacetGrid(tips, col="time",  hue="smoker")
#>>> g = (g.map(plt.scatter, "total_bill", "tip", edgecolor="w")
#...       .add_legend())

<matplotlib.legend.Legend at 0x2083a6e73c8>

flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("notebook", font_scale=1.5,)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

#_cond = ['POR', 'EXP']

#trends['POR'] = trends['POR'].apply(str)

fg = sns.FacetGrid(cell_trends, hue='POR', size= 9, aspect=2.3)#, hue_kws=dict(marker=["^", "v"]))
fg.map(plt.scatter, 'Experiment_ID', 'percentEff', alpha=.7,)
plt.xlim(560,680)
plt.ylim(12, 17)
plt.axhline(15.5, color='k', linestyle='--')
plt.axhline(14.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg1 = sns.FacetGrid(cell_trends, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'percentFF', alpha=.7)
plt.xlim(560,680)
plt.ylim(50, 80)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x2083baa30b8>

flatui = ["#3498db", "#e74c3c", "#2ecc71", "#95a5a6", "#34495e", "#9b59b6"]
sns.set(style='ticks')
sns.set_palette(flatui)
sns.set_context("notebook", font_scale=1.5,)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper

# create our boxplot which is drawn on an Axes object
plt.figure(2, figsize=(20,12))
plt.subplot(211)
bplot = sns.boxplot('Experiment_ID', 'percentEff', hue='POR', data=cell_trends, notch=True)

# We can call all the methods avaiable to Axes objects
#bplot.set_title(title, fontsize=20)
#bplot.set_xlabel('Experiment ID', fontsize=16)
bplot.set_ylabel('cell efficiency [%]', fontsize=16)
bplot.tick_params(axis='y', labelsize=12, which='minor')
bplot.set_ylim(0,20)
bplot.set_xlim(355.5,405.5)
plt.xticks(rotation=30)
plt.legend()

plt.subplot(212)
bplot2 = sns.boxplot('Experiment_ID', 'percentFF', data=cell_trends, hue='POR', notch=True)
bplot2.set_xlabel('Experiment ID', fontsize=16)
bplot2.set_ylabel('cell FF [%]', fontsize=16)
bplot2.tick_params(axis='both', labelsize=12)
bplot2.set_ylim(50,80)
bplot2.set_xlim(355.5,405.5)

#sns.despine(left=True) 
plt.xticks(rotation=30)
#plt.legend()
plt.show()

Voc + Jsc

sns.set(style='ticks')
sns.set_context("poster")
flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

fg = sns.FacetGrid(mod_trends, hue='POR', size=9, aspect=2.5)
fg.map(plt.scatter, 'Experiment_ID', 'modJsc', alpha=.7,)
plt.xlim(560,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 4, 25)])
plt.ylim(20, 29)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()


fg1 = sns.FacetGrid(mod_trends, hue='POR', size= 9, aspect=2.5)
fg1.map(plt.scatter, 'Experiment_ID', 'modVoc', alpha=.7, )
plt.xlim(560,680)
#plt.xlim([datetime.date(2015, 12, 5), datetime.date(2016, 4, 25)])
plt.ylim(.76, .86)
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x208658c2b00>

sns.set(style='ticks')
flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
sns.set_context("notebook", font_scale=1.5,)

# create our boxplot which is drawn on an Axes object
plt.figure(2, figsize=(20,12))
plt.subplot(211)
bplot = sns.boxplot('Experiment_ID', 'cellJsc', hue='POR', data=cell_trends, notch=True)

# We can call all the methods avaiable to Axes objects
#bplot.set_title(title, fontsize=20)
#bplot.set_xlabel('Experiment ID', fontsize=16)
bplot.set_ylabel('Cell Jsc [$ma/cm^2$]', fontsize=16)
bplot.tick_params(axis='both', labelsize=12)
bplot.set_ylim(22,30)
bplot.set_xlim(355.5,405.5)
plt.xticks(rotation=30)
#bplot.legend()

plt.subplot(212)
bplot2 = sns.boxplot('Experiment_ID', 'cellVoc', data=cell_trends, hue='POR', notch=True)
bplot2.set_xlabel('Experiment ID', fontsize=16)
bplot2.set_ylabel('Cell Voc [V]', fontsize=16)
bplot2.tick_params(axis='both', labelsize=12)
bplot2.set_ylim(0.6,0.9)
bplot2.set_xlim(355.5,405.5)


#sns.despine(left=True) 
plt.xticks(rotation=30)
plt.legend(loc='lower right')
plt.show()

Rs + Rsh

#using extrapolated Rs+Rsh (Roc/Rsc) from LIV curves -- dark R can be found in Rs/Rsh but need to filter for dark

sns.set(style='ticks')
sns.set_context("poster")
flatui2 = ["#95a5a6", "#9b59b6","#2ecc71", "#34495e", "#3498db", "#e74c3c",]
sns.set_palette(flatui2)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))

fg = sns.FacetGrid(mod_trends, hue='POR', size= 9, aspect=2.3)
fg.map(plt.scatter, 'Experiment_ID', 'modRs', alpha=.7,)
plt.xlim(560,680)
plt.ylim(.6, 4)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()


fg1 = sns.FacetGrid(mod_trends, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'modRsh', alpha=.7,)
plt.xlim(560,680)
plt.ylim(1e1, 1e5)
plt.yscale('log')
#plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

plt.savefig('shuntsbrah.png')

sns.set(style='ticks')
flatui2 = ["#95a5a6", "#9b59b6","#2ecc71", "#34495e", "#3498db", "#e74c3c",]
sns.set_palette(flatui2)
sns.set_context("notebook", font_scale=1.5,)
plt.rc('text', usetex=False)

# create our boxplot which is drawn on an Axes object
plt.figure(2, figsize=(20,12))
plt.subplot(211)
bplot = sns.boxplot('Experiment_ID', 'cellRs', hue='POR', data=cell_trends, notch=True)

# We can call all the methods avaiable to Axes objects
#bplot.set_title(title, fontsize=20)
#bplot.set_xlabel('Experiment ID', fontsize=16)
bplot.set_ylabel(r'Cell Rs [$\Omega$]', fontsize=16)
bplot.tick_params(axis='both', labelsize=12)
bplot.set_ylim(0.5,4.5)
bplot.set_xlim(355.5,405.5)
plt.xticks(rotation=30)
#plt.legend()

plt.subplot(212)
bplot2 = sns.boxplot('Experiment_ID', 'cellRsh', data=cell_trends, hue='POR', notch=True)
bplot2.set_xlabel('Experiment ID', fontsize=16)
bplot2.set_ylabel(r'Rsh [$\Omega$]', fontsize=16)
bplot2.tick_params(axis='both', labelsize=12)
bplot2.set_ylim(1,1e5)
bplot2.set_xlim(355.5,405.5)
plt.yscale('log')

#sns.despine(left=True) 
plt.xticks(rotation=30)
plt.legend()
plt.show()

sns.set_context("notebook", font_scale=1.5,)
plt.figure(figsize=(20,12))
#plt.subplot(211)
#bplot = sns.boxplot('Experiment_ID', 'cellRs_norm', hue='POR', data=cell_trends, notch=True)
#bplot.set_ylabel(r'Cell Rs [$\Omega$]', fontsize=16)
#bplot.tick_params(axis='both', labelsize=12)
#bplot.set_ylim(-1.5,15.5)
#bplot.set_xlim(350.5,390.5)
#plt.xticks(rotation=30)

fg = sns.FacetGrid(cell_trends, hue='POR', size= 9, aspect=2.3)
fg.map(plt.scatter, 'Experiment_ID', 'cellRs_norm', alpha=.7,)
plt.xlim(560,680)
plt.ylim(0, 20)
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg1 = sns.FacetGrid(cell_trends, hue='POR', size= 9, aspect=2.3)
fg1.map(plt.scatter, 'Experiment_ID', 'cellRsh_norm', alpha=.7,)
plt.xlim(560,680)
plt.ylim(1e0, 1e8)
plt.yscale('log')
plt.axhline(15.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x20861771198>

<matplotlib.figure.Figure at 0x208611d4358>

celltrendslatest = cell_trends[(cell_trends['Experiment_ID'] >= 623) & (cell_trends['Experiment_ID'] < 700)]
modtrendslatest = mod_trends[(mod_trends['Experiment_ID'] >= 623) & (mod_trends['Experiment_ID'] < 700)]


sns.set(style='ticks')
flatui2 = ["#95a5a6", "#9b59b6","#2ecc71", "#34495e", "#3498db", "#e74c3c",]
sns.set_palette(flatui2)
sns.set_context("notebook", font_scale=1.5,)

plt.figure(2, figsize=(12,10))
plt.subplot(211)
#v = sns.violinplot(x='Experiment_ID', y='cellRs', hue='POR', data=celltrendslatest, split=True, inner="quartile", bw=.5).set_ylim(-1, 10)
v = sns.violinplot(x='Experiment_ID', y='cellRs', hue='POR', data=celltrendslatest, split=True, inner="quartile", bw=.3).set_ylim(0, 20)
plt.xticks(rotation=30)

plt.subplot(212)
v2 = sns.violinplot(x='Experiment_ID', y='modRs', hue='POR', data=modtrendslatest, split=True, inner="quartile", bw=.3).set_ylim(0.4, 4)

plt.xticks(rotation=30)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
        51, 52, 53]), <a list of 54 Text xticklabel objects>)

#cds thickness vs efficiency
sns.set_palette("husl")
sns.set_context("notebook", font_scale=1.5,)

fg = sns.FacetGrid(cds, hue='Target Material', size= 9, aspect=2.4) #col='CdS Tool'
fg.map(plt.scatter, 'CdS Thickness (nm)', 'percentEff', alpha=.6,)
#plt.xlim(550,650)
plt.ylim(0, 17)
plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg1 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='Current (mA)') #col='CdS Tool'
fg1.map(plt.scatter, 'CdTe Thickness in Angstroms', 'percentEff', alpha=.6,)
plt.xlim(15000,30000)
plt.ylim(0, 17)
plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x208734ffeb8>

colorb=['#66c2a5','#fc8d62','#8da0cb','#e78ac3','#a6d854','#ffd92f']
sns.set_palette("husl")
sns.set_context("notebook", font_scale=1.5,)
fg = sns.FacetGrid(cds, hue='Target Material', size= 9, aspect=2.4) #col='CdS Tool'
fg.map(plt.scatter, 'Date_tm', 'CdS Thickness (nm)', alpha=.6,)
#plt.xlim(550,650)
plt.xlim([datetime.date(2015, 7, 5), datetime.date(2016, 5, 25)])
plt.ylim(20, 100)
#plt.axhline(15.5, color='k', linestyle='-')
plt.title("CdS thickness")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend(loc="lower right")

fg1 = sns.FacetGrid(cds, hue='Current (mA)', size= 9, aspect=2.4) #col='CdS Tool'
fg1.map(plt.scatter, 'Date_tm', 'CdTe Thickness in Angstroms', alpha=.6,)
#plt.xlim(550,650)
plt.xlim([datetime.date(2015, 7, 5), datetime.date(2016, 5, 25)])
plt.ylim(15000, 30000)
plt.title("CdTe thickness")
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend(loc="upper left")

<matplotlib.legend.Legend at 0x208611d42e8>

list(cds.columns.values)

['Sample ID',
 'Experiment_ID',
 'Description',
 'Date (IVT measure)',
 'Split1',
 'Split2',
 'Split3',
 'Comments',
 'POR',
 'Experiment ID',
 'Pallet Number',
 'Clean Process Start',
 'Clean Operator',
 'Substrate Type',
 'Clean Process ID',
 'Clean POR',
 'Clean Comment',
 'CdS Process Start',
 'CdS Operator',
 'CdS Tool',
 'Target Material',
 'Target Date',
 'Recipe ID',
 'Deposition Time (sec)/No of pass',
 'CdS POR',
 'CdS Thickness (nm)',
 'CdS Comment',
 'CdS Anneal Process Start',
 'CdS Anneal Operator',
 'CdS Anneal Tool',
 'CdS Anneal Recipe',
 'CdS Anneal POR',
 'CdS Anneal Comment',
 'CdTe 1 Process Start',
 'CdTe Operator',
 'CdTe Tool',
 'CdTe Solution ID',
 'CdTe Starting Potential mV',
 'CdTe QRP mV',
 'CdTe Total Charge C',
 'CdTe Chuck Temp C',
 'CdTe Solution Temp C',
 'Current (mA)',
 'CdTe Flow Rate L per min',
 'CdTe Flow Setting L per min',
 'CdTe Thickness in Angstroms',
 'CdTe POR',
 'CdTe Comment',
 'CdTe 2 Process Start',
 'CdTe 2 Operator',
 'CdTe 2 Tool',
 'CdTe 2 Solution ID',
 'CdTe 2 Starting Potential mV',
 'CdTe 2 QRP mV',
 'CdTe 2 Total Charge C',
 'CdTe 2 Chuck Temp C',
 'CdTe 2 Solution Temp C',
 'CdTe 2 Current (mA)',
 'CdTe 2 Flow Rate L per min',
 'CdTe 2 Flow Setting L per min',
 'CdTe 2 Thickness in Angstroms',
 'CdTe 2 POR',
 'CdTe 2 Comment',
 'CdTe Anneal Process Start',
 'CdTe Anneal Operator',
 'CdTe Anneal Tool',
 'CdTe Anneal Recipe',
 'CdTe Anneal POR',
 'CdTe Anneal Comment',
 'CdCl2 Process Start',
 'CdCl2  Operator',
 'CdCl2 Anneal Tool',
 'CdCl2  Recipe',
 'CdCl2 POR',
 'CdCl2 Comment',
 'Cu Process Start',
 'Cu Operator',
 'Cu Spray Tool',
 'Cu Anneal Tool',
 'Cu Recipe',
 'DAE Solution Date',
 'Cu Stock Date',
 'Cu Diluted Date',
 'Cu POR',
 'Cu Comment',
 'ZnTe Process Start',
 'ZnTe Operator',
 'ZnTe Tool',
 'ZnTe recipe',
 'ZnTe POR',
 'ZnTe Comment',
 'P1 Process Start',
 'P1 Operator',
 'P1 Tool',
 'P1 recipe',
 'P1 POR',
 'P1 Comment',
 'Photo Process Start',
 'Photo Operator',
 'Photo Tool',
 'Photo Anneal Oven',
 'Exposure Tool',
 'Photo Recipe',
 'Photoresist Batch Date',
 'Developer Solution Date',
 'Photo POR',
 'Photo Comment',
 'P2 Process Start',
 'P2 Operator',
 'P2 Tool',
 'P2 recipe',
 'P2 POR',
 'P2 Comment',
 'Sputter Process Start',
 'Sputter Operator',
 'Sputter Tool',
 'Sputter Recipe ID',
 'Sputter POR',
 'Sputter Slot',
 'Sputter Comment',
 'ARC Recipe',
 'ARC POR',
 'ARC Notes',
 'Metal Anneal Process Start',
 'Metal Anneal Operator',
 'Metal Anneal Tool',
 'Metal Anneal Temp',
 'Metal Anneal Recipe',
 'Metal Anneal POR',
 'Metal Anneal Comment',
 'P3 Process Start',
 'P3 Operator',
 'P3 Tool',
 'P3 recipe',
 'P3 POR',
 'P3 Comment',
 'Edge Delete Process Start',
 'Edge Delete Operator',
 'Edge Delete Tool',
 'EdgeDelete recipe',
 'Edge Delete POR',
 'Edge Delete Comment',
 'Additional Process Name',
 'Additional Process Start',
 'Additional Process Operator',
 'Additional Process Tool',
 'Additional Process Notes',
 'Additional Process Comments',
 'Cell_Area_mm2',
 'Cell_Count',
 'Cell_no',
 'Comment',
 'Current_Step',
 'Date_tm',
 'Eff',
 'Efficiency',
 'FF',
 'FillFactor',
 'I0_nA',
 'Iph_mA',
 'Isc_mA',
 'Measurement',
 'NumCells',
 'Pmp_fit_ratio',
 'Pmp_mW',
 'R_squared',
 'Roc_Ohm',
 'Rs_Ohm',
 'Rsc_Ohm',
 'Rsh_Ohm',
 'Sample_ID',
 'Temp_C',
 'User',
 'Vmp_V',
 'Voc_V',
 'n',
 'percentEff',
 'percentFF',
 'Voc',
 'Jsc',
 'Rs',
 'Rsh',
 'Unnamed: 0',
 'Sample',
 'XRF.Date',
 'XRF.Calibration',
 'CdTe_Thickness',
 'CdTe_Density',
 'Cd_Concentration',
 'Cd_Peak',
 'Cd_Background',
 'Te_Concentration',
 'Te_Peak',
 'Te_Background',
 'SnO2_Thickness',
 'SnO2_Density',
 'SnO2_Peak',
 'SnO2_Background',
 'SNR_Cd',
 'SNR_Te',
 'SNR_SnO2',
 'CdTe_Tool']

fg1 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg1.map(plt.scatter,'Cd_Concentration','percentEff', alpha=.6,)
plt.xlim(50,54)
plt.ylim(10, 17)
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg.map(plt.scatter,'Date_tm','Cd_Concentration', alpha=.6,)
#plt.xlim(50,54)
plt.xlim([datetime.date(2015, 12, 31), datetime.date(2016, 6, 25)])
plt.ylim(48, 54)
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg2 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg2.map(plt.scatter,'Date_tm','Te_Concentration', alpha=.6,)
#plt.xlim(50,54)
plt.xlim([datetime.date(2015, 12, 31), datetime.date(2016, 6, 25)])
plt.ylim(46, 50)
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

fg4 = sns.FacetGrid(cds, size= 9, aspect=2.4, hue='CdTe_Tool') #col='CdS Tool'
fg4.map(plt.scatter,'Cd_Concentration','Voc', alpha=.6,)
plt.xlim(50,54)
plt.ylim(0.7, 0.9)
#plt.axhline(15.5, color='k', linestyle='-')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x208625c36a0>

sns.color_palette("Paired")
sns.set_context("poster")


fg = sns.FacetGrid(cds, size= 6, aspect=1.5, col='CdTe_Tool', margin_titles=True, xlim=([datetime.date(2015, 7, 31), datetime.date(2016, 6, 25)]), ylim=(43, 58),hue='Cd_Concentration', palette = "coolwarm", legend_out=True) #col='CdS Tool'
plt.xticks(rotation=30)
fg.map(plt.scatter,'Date_tm', 'Cd_Concentration', alpha=.6,)
#plt.title("Cd Conc")
fg.set_xticklabels(rotation=30)
fg2 = sns.FacetGrid(cds, size= 6, aspect=1.5, margin_titles=True, xlim=([datetime.date(2015, 7, 31), datetime.date(2016, 6, 25)]), ylim=(43, 58),col='CdTe_Tool', hue='Te_Concentration', palette = "coolwarm", legend_out=True) #col='CdS Tool'
fg2.map(plt.scatter,'Date_tm', 'Te_Concentration', alpha=.6,)
#plt.title("Te Conc")
fg2.set_xticklabels(rotation=30)

<seaborn.axisgrid.FacetGrid at 0x2085edfbb38>

cds.plot(kind='scatter', x='percentEff', y='Cd_Concentration');
plt.xlim(14,16.5)
plt.ylim(50,52)

(50, 52)

sns.set_palette("husl")
sns.set_context("notebook", font_scale=1.5,)
fg = sns.FacetGrid(cds, hue='CdS Tool', size= 9, aspect=2.4)#, col='CdS Tool')
fg.map(plt.scatter, 'Date_tm', 'CdS Thickness (nm)', alpha=.6,)
#plt.xlim(550,650)
plt.xlim([datetime.date(2015, 7, 1), datetime.date(2016, 7, 1)])
plt.ylim(20, 100)
#plt.axhline(15.5, color='k', linestyle='-')
plt.title("CdS thickness by tool")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend(loc="lower right")

fg1 = sns.FacetGrid(cds, hue='CdS Tool', size= 9, aspect=2.4)#, col='CdS Tool')
fg1.map(plt.scatter, 'CdS Thickness (nm)', 'percentEff',alpha=.6,)
plt.xlim(0,90)
#plt.xlim([datetime.date(2015, 7, 1), datetime.date(2016, 7, 1)])
plt.ylim(10, 20)
plt.axhline(15.5, color='k', linestyle='-')
plt.title("CdS thickness v eff")
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend(loc="best")

<matplotlib.legend.Legend at 0x20878eea780>

stats -- cell & module

trends.describe()

#trends.sort_values(by='Date_tm')
#trends.iloc[:,[1,2,5,22,23,37, 38, 39]]

	Experiment_ID	CdS Thickness (nm)	CdTe Thickness in Angstroms	Cell_Area_mm2	Cell_Count	Cell_no	Eff	Efficiency	FF	FillFactor	I0_nA	Iph_mA	Isc_mA	NumCells	Pmp_fit_ratio	Pmp_mW	R_squared	Roc_Ohm	Rs_Ohm	Rsc_Ohm	Rsh_Ohm	Temp_C	Vmp_V	Voc_V	n	percentEff	percentFF	Voc	Jsc	Rs	Rsh
count	208689.000000	206467.000000	177827.000000	213055.000000	213055.000000	213055.000000	0	107673.000000	0	107254.000000	0	0	107674.000000	0	0	107674.000000	0	1.075630e+05	213045.000000	1.075640e+05	2.129980e+05	213055.000000	107674.000000	107243.000000	0	107673.000000	107254.000000	107243.000000	107673.000000	107562.000000	107563.000000
mean	574.375722	69.361703	20116.198929	288.430495	2.773096	11.613241	NaN	0.121412	NaN	0.770079	NaN	NaN	138.070958	NaN	NaN	186.968790	NaN	9.649615e+03	4269.103276	1.432593e+04	6.010033e+05	27.396815	1.586325	2.160156	NaN	12.141177	77.007896	0.777825	24.428920	694.213205	1702.930241
std	59.412598	10.567004	4729.988298	279.760775	5.834988	6.735397	NaN	0.038961	NaN	46.369988	NaN	NaN	18.020410	NaN	NaN	398.623351	NaN	2.241010e+06	361729.720488	2.506809e+06	5.405023e+07	1.599572	3.341442	4.636215	NaN	3.896126	4636.998764	0.931891	3.189341	104221.847812	133446.065470
min	467.000000	11.000000	1452.000000	0.000000	0.000000	1.000000	NaN	0.000000	NaN	0.000000	NaN	NaN	0.000000	NaN	NaN	0.000000	NaN	2.092800e-02	0.021370	2.370800e-02	1.858800e-02	0.000000	0.000000	-17.031892	NaN	0.000000	0.000000	-17.031892	0.000000	0.020928	0.023708
25%	523.000000	61.000000	15954.000000	5.650000	1.000000	5.000000	NaN	0.109729	NaN	0.585782	NaN	NaN	135.607570	NaN	NaN	64.347843	NaN	9.230520e-01	0.811687	4.189609e+01	8.519915e+01	26.346662	0.572149	0.783739	NaN	10.972900	58.578225	0.778275	23.977730	0.919684	39.803869
50%	575.000000	69.000000	21499.000000	565.000000	1.000000	11.000000	NaN	0.133861	NaN	0.666162	NaN	NaN	140.856078	NaN	NaN	77.576499	NaN	1.028762e+00	1.609269	8.671508e+01	4.343703e+02	27.242685	0.625336	0.810673	NaN	13.386100	66.616200	0.806583	24.918484	1.013788	77.851255
75%	625.000000	73.000000	22451.000000	565.000000	1.000000	17.000000	NaN	0.144471	NaN	0.698169	NaN	NaN	145.695777	NaN	NaN	83.545643	NaN	1.402134e+00	3.472676	2.363843e+02	8.703640e+03	28.408272	0.652007	0.823895	NaN	14.447100	69.816875	0.819910	25.784493	1.250414	172.078157
max	674.000000	170.000000	224018.000000	575.000000	23.000000	23.000000	NaN	1.169608	NaN	15182.410604	NaN	NaN	304.294011	NaN	NaN	2045.689848	NaN	7.277880e+08	71926771.933288	7.844196e+08	2.235724e+10	41.478232	18.102726	302.814585	NaN	116.960800	1518241.060400	302.814585	53.857347	33081270.805875	35655437.861323

cds.describe()

	Experiment_ID	CdS Thickness (nm)	CdTe Thickness in Angstroms	Cell_Area_mm2	Cell_Count	Cell_no	Eff	Efficiency	FF	FillFactor	I0_nA	Iph_mA	Isc_mA	NumCells	Pmp_fit_ratio	Pmp_mW	R_squared	Roc_Ohm	Rs_Ohm	Rsc_Ohm	Rsh_Ohm	Temp_C	Vmp_V	Voc_V	n	percentEff	percentFF	Voc	Jsc	Rs	Rsh	Unnamed: 0	CdTe_Thickness	CdTe_Density	Cd_Concentration	Cd_Peak	Cd_Background	Te_Concentration	Te_Peak	Te_Background	SnO2_Thickness	SnO2_Density	SnO2_Peak	SnO2_Background	SNR_Cd	SNR_Te	SNR_SnO2
count	8726.000000	8632.000000	7415.000000	8892.000000	8892.000000	8892.000000	0	8892.000000	0	8885.000000	0	0	8892.000000	0	0	8892.000000	0	8.892000e+03	8892.000000	8.892000e+03	8.890000e+03	8892.000000	8892.000000	8885.000000	0	8892.000000	8885.000000	8885.000000	8891.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8890.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000	8892.000000
mean	573.954848	69.329217	20100.625354	565.016262	22.000900	1.998875	NaN	0.116928	NaN	2.321044	NaN	NaN	137.842228	NaN	NaN	1453.671153	NaN	1.134716e+05	40965.567908	1.597785e+05	2.967558e+06	28.199222	12.382865	16.930440	NaN	11.692838	232.104409	0.769532	24.393782	5157.802157	7262.651000	4789.191408	19894.175596	7.276024	51.100382	4067.069276	174.599978	48.910619	4002.174989	264.574112	4351.730859	7.467984	1147.925439	101.545659	0.959709	0.936751	0.918941
std	59.908271	10.362223	4727.658221	7.352214	0.029983	0.033518	NaN	0.026571	NaN	161.062580	NaN	NaN	15.621346	NaN	NaN	330.485938	NaN	7.793541e+06	1340162.251675	8.714536e+06	2.382059e+08	2.031126	1.952671	1.825943	NaN	2.657143	16106.258021	0.082971	2.762849	354251.873780	396115.269682	3168.007310	3452.190420	0.032650	1.264415	609.071508	120.540217	1.030037	571.885129	119.399764	749.146194	0.549916	42.330370	30.200288	0.030324	0.034454	0.026961
min	467.000000	11.000000	1452.000000	0.000000	22.000000	1.000000	NaN	0.000000	NaN	0.087460	NaN	NaN	0.000000	NaN	NaN	0.000000	NaN	9.590290e-01	0.954137	1.665353e+00	6.095970e-01	22.406696	0.000000	0.032261	NaN	0.000000	8.746000	0.001466	0.000000	0.043592	0.075698	2250.000000	52.200000	6.887000	33.700000	12.000000	27.000000	25.100000	0.000000	108.000000	0.000000	7.300000	0.000000	54.000000	0.083922	0.000000	0.000000
25%	523.000000	61.000000	15888.000000	565.000000	22.000000	2.000000	NaN	0.109320	NaN	0.579439	NaN	NaN	135.992889	NaN	NaN	1358.700383	NaN	2.186055e+01	15.536958	1.109776e+03	1.868506e+03	26.606468	11.799924	16.888009	NaN	10.932025	57.943900	0.767637	24.049901	0.993661	50.444356	3376.750000	15685.775000	7.266000	50.700000	3383.750000	84.000000	48.700000	3356.000000	199.000000	3467.675000	7.300000	1126.000000	83.000000	0.944705	0.926283	0.910333
50%	574.000000	69.000000	21497.000000	565.000000	22.000000	2.000000	NaN	0.124074	NaN	0.630244	NaN	NaN	140.473287	NaN	NaN	1542.244020	NaN	2.448674e+01	17.894990	2.000049e+03	3.293265e+03	28.014429	12.931437	17.510069	NaN	12.407350	63.024400	0.795912	24.853911	1.113034	90.911312	4471.000000	21423.250000	7.274000	51.000000	4304.000000	126.000000	49.000000	4225.000000	242.000000	4851.200000	7.300000	1153.000000	92.000000	0.968597	0.937376	0.925212
75%	626.000000	73.000000	22454.000000	565.000000	22.000000	2.000000	NaN	0.133886	NaN	0.666657	NaN	NaN	144.830673	NaN	NaN	1664.745813	NaN	2.874862e+01	20.799528	4.337344e+03	6.891332e+03	29.963842	13.612241	17.815288	NaN	13.388625	66.665700	0.809776	25.638386	1.306756	197.152015	5753.000000	22382.125000	7.281000	51.300000	4485.000000	253.000000	49.300000	4404.000000	317.000000	5001.300000	7.300000	1172.000000	116.000000	0.977615	0.954388	0.931965
max	674.000000	112.000000	224018.000000	575.000000	23.000000	2.000000	NaN	0.164577	NaN	15182.410604	NaN	NaN	171.986190	NaN	NaN	2045.689848	NaN	7.277880e+08	71926771.933288	7.844196e+08	2.235724e+10	41.478232	18.102726	20.809271	NaN	16.457700	1518241.060400	0.945876	30.440034	33081270.805875	35655437.861323	37691.000000	32589.600000	8.650000	100.000000	9562.000000	2228.000000	66.300000	6176.000000	3426.000000	5307.300000	9.268000	1282.000000	706.000000	0.989588	0.979863	0.958365

#import seaborn as sns
sns.set(style="ticks", context="talk")

plt.figure(2, figsize=(12,10))

# Make a custom sequential palette using the cubehelix system
pal = sns.cubehelix_palette(4, 1.5, .75, light=.6, dark=.2)

# Plot tip as a function of toal bill across days
b = sns.lmplot(x='Experiment_ID', y='percentEff', col='POR', data=cell_trends,
               palette=pal, size=7)

# Use more informative axis labels than are provided by default
b.set_axis_labels("Experiment ID", "Efficiency [%]")
plt.xlim(500,680)
plt.ylim(4, 18)

(4, 18)

<matplotlib.figure.Figure at 0x20861196160>

#dff.groupby('B').filter(lambda x: len(x['C']) > 2)
#groupedcells = cell_trends.groupby(['Experiment_ID', 'POR', 'Measurement']).filter(lambda x: )
#groupedcells = groupedcells.drop('Eff')

#del celldf
#del groupedcell 

celldf = cell_trends.drop(['Eff', 'NumCells', 'FF', 'I0_nA', 'n', 'Pmp_fit_ratio'], axis=1)
celldf = celldf[~celldf['Measurement'].str.contains('.*Dark.*', na = False)]
celldf.head()

moddf = mod_trends.drop(['Eff', 'NumCells', 'FF', 'I0_nA', 'n', 'Pmp_fit_ratio'], axis=1)
moddf = moddf[~moddf['Measurement'].str.contains('.*Dark.*', na = False)]
moddf.head()

#setting up grouped median and mean dfs
groupmoddf = moddf.groupby('Experiment_ID', as_index=False)
modmedian = groupmoddf.median()
modmean = groupmoddf.mean()


groupcelldf = celldf.groupby('Experiment_ID', as_index=False)
cellmedian = groupcelldf.median()
cellmean = groupcelldf.mean()

list(cellmedian.columns.values)

['Experiment_ID',
 'Cell_Area_mm2',
 'Cell_Count',
 'Cell_no',
 'Efficiency',
 'FillFactor',
 'Isc_mA',
 'Pmp_mW',
 'Roc_Ohm',
 'Rs_Ohm',
 'Rsc_Ohm',
 'Rsh_Ohm',
 'Temp_C',
 'Vmp_V',
 'Voc_V',
 'percentEff',
 'percentFF',
 'cellVoc',
 'cellJsc',
 'cellRs',
 'cellRs_norm',
 'cellRsh',
 'cellRsh_norm',
 'shunt_count']

cellmedian.tail(50)

	Experiment_ID	Cell_Area_mm2	Cell_Count	Cell_no	Efficiency	FillFactor	Isc_mA	Pmp_mW	Roc_Ohm	Rs_Ohm	Rsc_Ohm	Rsh_Ohm	Temp_C	Vmp_V	Voc_V	percentEff	percentFF	cellVoc	cellJsc	cellRs	cellRs_norm	cellRsh	cellRsh_norm	shunt_count
361	627.0	565	1	12.5	0.132584	0.639284	149.193882	74.910362	1.010021	0.708271	84.557765	130.424583	29.494025	0.592627	0.784611	13.25845	63.92840	0.784611	26.405997	0.708271	4.001728	84.557765	477.751372	1
362	628.0	565	1	12.5	0.141357	0.690786	143.584439	79.866993	0.952739	0.784082	88.677359	129.246214	28.857863	0.624304	0.811456	14.13575	69.07865	0.811456	25.413175	0.784082	4.430063	88.677359	501.027078	1
363	629.0	565	1	12.5	0.146932	0.711961	142.090173	83.016682	0.899244	0.765256	98.314758	139.500867	28.674636	0.647490	0.822606	14.69320	71.19615	0.822606	25.148703	0.765256	4.323694	98.314758	555.478383	1
364	630.0	565	1	12.5	0.144389	0.690230	145.803833	81.579531	0.951205	0.599957	100.623593	150.475259	28.225439	0.627999	0.808606	14.43885	69.02305	0.808606	25.805988	0.599957	3.389754	100.623593	568.523303	1
365	631.0	565	1	12.5	0.102114	0.558959	140.601231	57.694685	1.383992	0.768769	44.365947	68.404459	27.914561	0.508055	0.728556	10.21145	55.89595	0.728556	24.885174	0.768769	4.343545	44.365947	250.667601	1
366	632.0	565	1	12.5	0.140490	0.692375	142.031866	79.377235	0.965947	0.621609	91.567498	117.500822	28.538697	0.619457	0.802391	14.04905	69.23750	0.802391	25.138383	0.621609	3.512094	91.567498	517.356364	1
367	633.0	565	1	12.5	0.124440	0.642325	139.386199	70.308159	1.105875	0.754660	83.015210	118.602105	27.740596	0.584442	0.782776	12.44395	64.23250	0.782776	24.670124	0.754660	4.263829	83.015210	469.035939	1
368	634.0	565	1	12.5	0.136396	0.676889	141.430625	77.063790	1.008544	0.608716	82.197763	127.693438	28.648980	0.616552	0.802884	13.63960	67.68890	0.802884	25.031969	0.608716	3.439245	82.197763	464.417361	1
369	635.0	565	1	12.5	0.142325	0.698703	141.143932	80.413620	0.961026	0.580410	89.414747	136.632408	28.349764	0.629105	0.810807	14.23250	69.87030	0.810807	24.981227	0.580410	3.279314	89.414747	505.193321	1
370	636.0	565	1	12.5	0.137274	0.686476	140.239887	77.559891	0.988635	0.603621	82.098744	133.747260	28.576597	0.622341	0.800911	13.72745	68.64755	0.800911	24.821219	0.603621	3.410459	82.098744	463.857904	1
371	637.0	565	1	12.5	0.142547	0.671699	148.476017	80.539163	0.992432	0.669817	85.894548	165.489017	28.696174	0.612611	0.803257	14.25470	67.16990	0.803257	26.278941	0.669817	3.784463	85.894548	485.304196	1
372	638.0	565	1	12.5	0.143819	0.689034	144.878057	81.257504	0.957874	0.602259	114.927023	184.452990	29.257390	0.633657	0.813766	14.38185	68.90335	0.813766	25.642134	0.602259	3.402761	114.927023	649.337677	1
373	639.0	565	1	12.5	0.150138	0.696111	148.045981	84.827911	0.931627	0.624627	100.016484	159.253322	28.463598	0.639417	0.819908	15.01380	69.61110	0.819908	26.202828	0.624627	3.529143	100.016484	565.093135	1
374	640.0	565	1	12.5	0.141750	0.679348	146.057670	80.088923	0.981818	0.617672	99.358724	151.138895	30.314540	0.618615	0.806136	14.17500	67.93485	0.806136	25.850915	0.617672	3.489850	99.358724	561.376791	1
375	641.0	565	1	12.5	0.147636	0.699380	143.852419	83.414215	0.944848	0.634729	108.411648	159.423194	28.660160	0.645366	0.824806	14.76355	69.93805	0.824806	25.460605	0.634729	3.586219	108.411648	612.525811	1
376	642.0	565	1	12.5	0.137485	0.679116	141.612089	77.679045	1.017533	0.771366	95.400611	161.181389	28.035390	0.620299	0.803124	13.74850	67.91160	0.803124	25.064087	0.771366	4.358215	95.400611	539.013452	1
377	643.0	565	1	12.5	0.147111	0.692260	147.516430	83.117625	0.953971	0.633407	88.833362	141.685823	28.129476	0.627787	0.812333	14.71110	69.22605	0.812333	26.109103	0.633407	3.578750	88.833362	501.908498	1
378	644.0	565	1	12.5	0.136425	0.686475	140.027116	77.080302	1.001514	0.683584	101.578835	134.691465	28.778786	0.614362	0.796942	13.64250	68.64745	0.796942	24.783560	0.683584	3.862250	101.578835	573.920421	1
379	645.0	565	1	12.5	0.137999	0.697676	136.989936	77.969603	0.983396	0.584129	70.083104	91.844222	29.206105	0.625613	0.805082	13.79995	69.76755	0.805082	24.246006	0.584129	3.300326	70.083104	395.969538	1
380	646.0	565	1	12.5	0.090370	0.568409	128.461984	51.059167	1.404192	0.975423	45.465632	73.754728	28.569064	0.504923	0.717822	9.03700	56.84090	0.717822	22.736634	0.975423	5.511140	45.465632	256.880821	1
381	647.0	565	1	12.5	0.136010	0.693024	138.042163	76.845800	0.994728	0.593888	90.449371	120.029011	28.335755	0.616219	0.800467	13.60105	69.30240	0.800467	24.432241	0.593888	3.355467	90.449371	511.038946	1
382	648.0	565	1	12.5	0.143885	0.725269	136.828521	81.294843	0.911717	0.528296	85.683952	121.286278	29.519425	0.650888	0.821260	14.38850	72.52690	0.821260	24.217437	0.528296	2.984872	85.683952	484.114326	1
383	648.5	565	1	12.5	0.087969	0.518056	138.062083	49.702251	1.349876	0.757208	42.611576	65.998612	28.520923	0.468452	0.684062	8.79685	51.80560	0.684062	24.435767	0.757208	4.278222	42.611576	240.755404	1
384	649.0	565	1	12.5	0.143105	0.682624	145.132140	80.854685	0.995383	0.663665	93.231183	153.838864	28.484786	0.614715	0.808720	14.31055	68.26235	0.808720	25.687105	0.663665	3.749707	93.231183	526.756181	1
385	650.0	565	1	12.5	0.140171	0.680219	142.773931	79.196411	1.017154	0.624669	87.925916	129.891908	28.505621	0.609938	0.800253	14.01710	68.02195	0.800253	25.269722	0.624669	3.529380	87.925916	496.781425	1
386	651.0	565	1	12.5	0.123053	0.626853	142.549464	69.525486	1.139439	0.845143	69.428857	122.551592	27.426984	0.571470	0.775817	12.30535	62.68530	0.775817	25.229994	0.845143	4.775058	69.428857	392.273045	1
387	652.0	565	1	12.5	0.144307	0.684414	145.626461	81.533411	0.981824	0.713820	94.675865	154.815231	28.437466	0.627111	0.814228	14.43070	68.44145	0.814228	25.774595	0.713820	4.033080	94.675865	534.918634	1
388	653.0	565	1	12.5	0.142132	0.674899	146.207752	80.304654	0.998545	0.672297	68.574269	99.281900	28.224026	0.615374	0.808985	14.21320	67.48995	0.808985	25.877478	0.672297	3.798481	68.574269	387.444623	1
389	653.5	565	1	12.5	0.120845	0.630062	143.714049	68.277713	1.143823	0.946480	76.303511	131.907608	28.399560	0.552401	0.758409	12.08455	63.00620	0.758409	25.436115	0.946480	5.347612	76.303511	431.114837	1
390	654.0	565	1	12.5	0.144421	0.677351	146.072870	81.598158	0.993437	0.674803	75.733505	116.067399	29.458980	0.614875	0.809513	14.44215	67.73510	0.809513	25.853605	0.674803	3.812637	75.733505	427.894303	1
391	655.0	565	1	12.5	0.110152	0.605337	136.850364	62.235745	1.246568	1.230839	59.983202	89.440748	28.193531	0.549676	0.757748	11.01520	60.53370	0.757748	24.221303	1.230839	6.954240	59.983202	338.905091	1
392	656.0	565	1	12.5	0.138739	0.678978	142.599831	78.387707	0.986940	0.741551	76.484234	129.753612	27.669446	0.621168	0.806709	13.87395	67.89780	0.806709	25.238908	0.741551	4.189760	76.484234	432.135919	1
393	657.0	565	1	12.5	0.105771	0.575846	138.398945	59.760266	1.199824	0.586460	49.333481	77.491176	28.554468	0.550185	0.754962	10.57705	57.58465	0.754962	24.495388	0.586460	3.313499	49.333481	278.734168	1
394	658.0	565	1	12.5	0.141870	0.690893	142.808346	80.156589	0.953776	0.617950	84.882246	150.264488	28.596488	0.631814	0.807794	14.18700	69.08930	0.807794	25.275813	0.617950	3.491418	84.882246	479.584690	1
395	659.0	565	1	12.5	0.135508	0.652286	146.244275	76.561847	1.032648	0.643625	81.041004	114.387225	28.789378	0.610183	0.802101	13.55080	65.22860	0.802101	25.883942	0.643625	3.636481	81.041004	457.881673	1
396	660.0	565	1	12.5	0.137952	0.665039	148.513832	77.942494	1.001893	0.610599	100.557827	151.481536	28.406389	0.599865	0.790351	13.79515	66.50390	0.790351	26.285634	0.610599	3.449887	100.557827	568.151720	1
397	661.0	565	1	12.5	0.129592	0.628459	153.994849	73.219881	1.015724	0.745998	64.641213	114.464266	27.577439	0.542725	0.737969	12.95925	62.84585	0.737969	27.255726	0.745998	4.214889	64.641213	365.222856	1
398	662.0	565	1	12.5	0.144142	0.674697	151.965353	81.440365	0.939859	0.596730	86.952493	143.202753	27.480477	0.598472	0.789100	14.41420	67.46975	0.789100	26.896523	0.596730	3.371525	86.952493	491.281585	1
399	663.0	565	1	12.5	0.147334	0.689291	150.405361	83.243704	0.915512	0.554335	92.084619	139.436224	28.270061	0.619595	0.800342	14.73340	68.92915	0.800342	26.620418	0.554335	3.131993	92.084619	520.278095	1
400	664.0	565	1	12.5	0.149835	0.702952	148.509478	84.656742	0.894684	0.522725	91.005786	128.320918	28.493026	0.639544	0.815551	14.98350	70.29520	0.815551	26.284863	0.522725	2.953396	91.005786	514.182694	1
401	665.0	565	1	12.5	0.152390	0.709060	149.583823	86.100161	0.875645	0.502842	94.850712	155.590936	28.053878	0.643904	0.816438	15.23895	70.90605	0.816438	26.475013	0.502842	2.841057	94.850712	535.906520	1
402	666.0	565	1	12.5	0.139610	0.693807	141.770377	78.879906	0.947799	0.538082	83.301406	142.682949	27.639053	0.618621	0.797884	13.96105	69.38075	0.797884	25.092102	0.538082	3.040163	83.301406	470.652944	1
403	667.0	565	1	12.5	0.137221	0.680728	138.280984	77.529812	0.987658	0.568812	68.267776	113.754778	27.266726	0.607863	0.796278	13.72210	68.07280	0.796278	24.474510	0.568812	3.213785	68.267776	385.712934	1
404	668.0	565	1	12.5	0.137683	0.692756	139.693502	77.790898	0.959588	0.585396	85.379415	133.153914	27.162427	0.627911	0.800642	13.76830	69.27555	0.800642	24.724514	0.585396	3.307490	85.379415	482.393698	1
405	669.0	565	1	12.5	0.138561	0.690056	139.799526	78.286599	0.954433	0.575036	94.294202	150.326425	27.016157	0.630775	0.805542	13.85605	69.00565	0.805542	24.743279	0.575036	3.248951	94.294202	532.762238	1
406	670.0	565	1	12.5	0.145157	0.718981	141.864841	82.013880	0.880149	0.494681	97.922015	144.996322	26.927984	0.640707	0.806903	14.51575	71.89815	0.806903	25.108821	0.494681	2.794950	97.922015	553.259385	1
407	671.0	565	1	12.5	0.146064	0.702975	145.689955	82.525721	0.895292	0.534550	101.039327	142.962980	26.353267	0.634878	0.804865	14.60635	70.29755	0.804865	25.785833	0.534550	3.020208	101.039327	570.872200	1
408	672.0	565	1	12.5	0.134747	0.681879	143.597210	76.131876	0.998141	0.556910	104.832915	155.489352	26.915370	0.596496	0.780253	13.47470	68.18795	0.780253	25.415435	0.556910	3.146542	104.832915	592.305970	1
409	673.0	565	1	12.5	0.135535	0.670691	141.734667	76.577265	0.987384	0.575906	71.973229	110.423284	26.450097	0.612645	0.797773	13.55350	67.06910	0.797773	25.085782	0.575906	3.253869	71.973229	406.648741	1
410	674.0	565	1	12.5	0.144458	0.719219	140.926579	81.619089	0.869694	0.523352	73.921125	108.210358	26.444790	0.646990	0.820998	14.44585	71.92190	0.820998	24.942757	0.523352	2.956942	73.921125	417.654356	1

#ax = df1.plot() method to plot 2 dfs on same plot
#df2.plot(ax=ax)

plt.figure(2, figsize=(15,10))
ax = cellmedian.plot( y=[15], label = 'median Eff')
cellmean.plot(ax=ax, y=[15], label = 'mean Eff' )
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines[:2], labels[:2], loc='best')
plt.ylim(7,17)
plt.xlim(250, 420)

(250, 420)

<matplotlib.figure.Figure at 0x2094211c898>

plt.figure(2, figsize=(14,8))

ax = modmedian.plot(kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'mod med', color = 'DarkBlue', title = 'mod efficiencies')
modmean.plot(ax=ax, x=['Experiment_ID'], y=['percentEff'], label = 'mod mean', kind ='scatter', color = 'DarkGreen')
cellmedian.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'cell med', color = 'LightBlue', title = 'efficiencies')
cellmean.plot(ax=ax, x=['Experiment_ID'], y=['percentEff'], label = 'cell mean', kind ='scatter', color = 'LightGreen')
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines[:4], labels[:4], loc='upper right')
plt.ylim(8,18)
plt.xlim(560, 680)

(560, 680)

<matplotlib.figure.Figure at 0x2085df44160>

#median = median.reset_index()
#mean = mean.reset_index()
plt.figure(figsize=(12,10))

ax = modmedian.plot(kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'mod med', color = 'Red')
cellmedian.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'cell med', color = 'LightBlue')
lines, labels = ax.get_legend_handles_labels()
plt.ylim(8,18)
plt.xlim(500, 680)
plt.title('efficiencies')

<matplotlib.text.Text at 0x2087316f588>

<matplotlib.figure.Figure at 0x20867172be0>

plt.figure(figsize=(12,10))

ax = modmean.plot(kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'mod mean', color = 'Red')
cellmean.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['percentEff'], label = 'cell mean', color = 'LightBlue')
lines, labels = ax.get_legend_handles_labels()
plt.ylim(8,18)
plt.xlim(500, 680)
plt.title('efficiencies')

<matplotlib.text.Text at 0x2085d8cea90>

<matplotlib.figure.Figure at 0x208731e6518>

allmed1 = pd.merge(modmedian, cellmedian, on='Experiment_ID')
allmed1['del_percentEff'] = allmed1['percentEff_x'] - allmed1['percentEff_y']
allmed1['del_percentFF'] = allmed1['percentFF_x'] - allmed1['percentFF_y']

plt.figure(2, figsize=(25,20))

ax = allmed1.plot(kind = 'scatter', x=['Experiment_ID'], y=['del_percentFF'], label = 'del FF mod-cell', color = 'DarkRed')
allmed1.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['del_percentEff'], label = 'del eff mod-cell', color = 'LightBlue')
#allmed1.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['del_voc'], label = 'del eff mod-cell med', color = 'LightBlue')
lines, labels = ax.get_legend_handles_labels()
plt.axhline(0, color='k', linestyle='--')
plt.axhline(-5, color='b', linestyle='--')
plt.axhline(-10, color='r', linestyle='--')
plt.ylim(-12,8)
plt.xlim(500, 680)
plt.title('median del')

<matplotlib.text.Text at 0x20941be7550>

<matplotlib.figure.Figure at 0x20873410940>

#del allmed
#allmed = pd.merge(modmedian, cellmedian, on='Experiment_ID')
allmed = pd.concat([modmedian, cellmedian])

allmean1 = pd.merge(modmean, cellmean, on='Experiment_ID')
allmean1['del_percentEff'] = allmean1['percentEff_x'] - allmean1['percentEff_y']
allmean1['del_percentFF'] = allmean1['percentFF_x'] - allmean1['percentFF_y']

plt.figure(figsize=(20,20))
ax = allmean1.plot(kind = 'scatter', x=['Experiment_ID'], y=['del_percentFF'], label = 'del FF mod-cell', color = 'DarkRed')
allmean1.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['del_percentEff'], label = 'del eff mod-cell mean', color = 'LightBlue')
#allmed1.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['del_voc'], label = 'del eff mod-cell med', color = 'LightBlue')
lines, labels = ax.get_legend_handles_labels()
plt.axhline(0, color='k', linestyle='--')
plt.axhline(-5, color='b', linestyle='--')
plt.axhline(-10, color='r', linestyle='--')
plt.ylim(-4,4)
plt.xlim(500, 680)
plt.title('mean del')

<matplotlib.text.Text at 0x208616795c0>

<matplotlib.figure.Figure at 0x209619ef3c8>

allmed1['del_voc'] = allmed1['modVoc'] - allmed1['cellVoc']
allmed1['del_jsc'] = allmed1['modJsc'] - allmed1['cellJsc']

plt.figure(figsize=(18,10))


ax = allmed1.plot(kind = 'scatter', x=['Experiment_ID'], y=['del_jsc'], label = 'jsc', color = 'Red')
allmed1.plot(ax=ax, kind='scatter', x=['Experiment_ID'], y=['del_voc'], label = 'voc', color = 'Green')
lines, labels = ax.get_legend_handles_labels()
plt.axhline(0, color='k', linestyle='--')
plt.axhline(-1, color='b', linestyle='--')
plt.axhline(-2, color='r', linestyle='--')
plt.ylim(-1,0.5)
plt.xlim(600, 680)
plt.title('dels')

<matplotlib.text.Text at 0x2085c4e1978>

<matplotlib.figure.Figure at 0x2087353b1d0>

sns.set(style='ticks')
sns.set_context("poster")
flatui1 = ["#2ecc71", "#34495e", "#95a5a6", "#9b59b6", "#3498db", "#e74c3c",]
sns.set_palette(flatui1)
#fig, ax = plt.subplots(figsize=(15, 15))
# the size of A4 paper
#plt.figure(figsize=(20, 20))


fg = sns.FacetGrid(allmed, hue='Cell_Count', size= 9, aspect=2.3,despine=False)#, hue_kws=dict(marker=["^", "v"]))
fg.map(plt.scatter, 'Experiment_ID', 'percentEff', alpha=.7,)
plt.xlim(250,680)
plt.ylim(6, 18)
#plt.axhline(15.5, color='k', linestyle='--')
#plt.axhline(14.5, color='k', linestyle='--')
plt.grid(b=True, which='both', color='0.65',linestyle='-')
plt.legend()

<matplotlib.legend.Legend at 0x2083bb2f9b0>

#del groupedcells

celltrendstrimmed = cell_trends[(cell_trends['Experiment_ID'] >= 400) & (cell_trends['Experiment_ID'] < 700)]
groupedcells = celltrendstrimmed.groupby(['Experiment_ID', 'POR'],as_index=False)

groupedmean = groupedcells.aggregate(np.mean)
#groupedmean = groupedmean.reset_index()

#groupedmean
#groupedmean.info()
#print(groupedmean.columns)

sns.set_context("notebook")
plt.figure(figsize=(10, 10))
p = sns.lmplot(x="Experiment_ID", y="percentEff",  data=groupedmean, col ="POR", size=8)
plt.ylim(6,18)

(6, 18)

<matplotlib.figure.Figure at 0x208610af160>

b = pd.DataFrame(mod_trends.groupby(['Experiment_ID'], as_index=False)['shunt_count'].sum())

b.sort_values(by='Experiment_ID')
b.head()
plt.figure(figsize=(15, 8))

sns.barplot(x='Experiment_ID', y='shunt_count', data=b)
plt.xlim(200.5, 405.5)
plt.ylim(0,30)
plt.xticks(rotation=30)

(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
        195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
        208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
        221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
        247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
        260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
        273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
        286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
        299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
        312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
        325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
        338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
        351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
        364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
        377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
        390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
        403, 404, 405, 406, 407, 408, 409, 410]),
 <a list of 411 Text xticklabel objects>)

plt.figure(figsize=(15, 8))
b.plot(x='Experiment_ID', y='shunt_count', kind='bar')
plt.xlim(350.5, 405.5)
plt.ylim(0,40)

(0, 40)

<matplotlib.figure.Figure at 0x20863f2d6d8>

b1 = pd.DataFrame(cell_trends.groupby(['Experiment_ID'], as_index=False)['shunt_count'].sum())

b1.sort_values(by='Experiment_ID')
b1.head()
plt.figure(figsize=(15, 8))

sns.barplot(x='Experiment_ID', y='shunt_count', data=b1)
plt.axhline(7*20, color='k', linestyle='--')
plt.xlim(330.5, 405.5)
plt.ylim(0,400)
plt.xticks(rotation=30)


#b1.plot(x='Experiment_ID', y='shunt_count', kind='bar')

(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,
        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,
        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,
        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,
        143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,
        156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
        169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
        182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,
        195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,
        208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,
        221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,
        247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259,
        260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272,
        273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
        286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298,
        299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311,
        312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324,
        325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337,
        338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350,
        351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363,
        364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376,
        377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389,
        390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,
        403, 404, 405, 406, 407, 408, 409, 410]),
 <a list of 411 Text xticklabel objects>)

plt.figure(figsize=(15, 8))
sns.barplot(x='Experiment_ID', y='shunt_count', data=b1)
plt.axhline(7*20, color='k', linestyle='--')
plt.xlim(200.5, 400.5)
plt.ylim(0,400)

(0, 400)

#del groupedcells
#del groupedcells1

groupedcells = cell_trends.groupby(['Experiment_ID', 'POR'], as_index=False )
groupedcells = groupedcells.describe().unstack()
groupedcells1 = groupedcells.reset_index()
groupedcells1.head()

	index	Experiment_ID								Cell_Area_mm2								Cell_Count								Cell_no								Eff								Efficiency								FF								FillFactor								Isc_mA								NumCells								Pmp_fit_ratio								Pmp_mW								Roc_Ohm								Rs_Ohm								Rsc_Ohm								Rsh_Ohm								Temp_C								Vmp_V								Voc_V								n								percentEff								percentFF								cellVoc								cellJsc								cellRs								cellRs_norm								cellRsh								cellRsh_norm								shunt_count
		count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max	count	mean	std	min	25%	50%	75%	max
0	0	368	256	0	256	256	256	256	256	368	588.485812	1.505314e+02	6.273	627.30	627.3	627.3	627.3	368	1	0	1	1	1	1	1	368	12.000000	6.642281	1	6	12	18	23	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.118582	0.027115	0.026300	0.110575	0.1302	0.136525	0.148900	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.630413	0.104214	0.303300	0.600525	0.679050	0.701825	0.743845	184	150.484939	5.936031	129.320000	146.952500	150.570000	153.6625	168.35000	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	161	0.993911	0.008820	0.9644	0.989400	0.99410	0.999100	1.0224	184	74.385099	17.013028	16.500000	69.375000	81.65	85.625	93.400000	184	0.890946	0.475543	0.533	0.64275	0.7490	0.879750	2.975000	322	0.398370	0.393988	0	0.16900	0.2900	0.46625	2.418	184	6085358.901740	7.382826e+07	16.100000	89.000000	127.600000	153.225000	1.000000e+09	322	6.088264e+16	7.382188e+17	13.7	123.025	173.75	2.668445e+04	1.273945e+19	368	28.352258	1.208248	25.91	27.3700	28.200000	29.404689	31.041384	184	0.579048	0.096554	0.219000	0.537525	0.62655	0.641415	0.687802	184	0.770373	0.078765	0.406200	0.743375	0.801850	0.816618	0.83060	322	3.060124	2.108376	1.42	2.0125	2.39	2.980	15.36	184	11.858217	2.711472	2.6300	11.057500	13.02	13.6525	14.8900	184	63.041343	10.421384	30.3300	60.052500	67.90500	70.1825	74.3845	184	0.770373	0.078765	0.406200	0.743375	0.801850	0.816618	0.83060	184	23.989310	0.946283	20.615336	23.426192	24.002869	24.495855	26.837239	322	0.398370	0.393988	0	0.16900	0.2900	0.46625	2.418	322	2.498972	2.471488	0	1.060137	1.819170	2.924786	15.168114	184	6085358.901740	7.382826e+07	16.100000	89.000000	127.600000	153.225000	1.000000e+09	184	3.817346e+07	4.631247e+08	100.995300	558.297000	800.434800	961.180425	6.273000e+09	26	1	0	1	1	1	1	1
1	1	382	256	0	256	256	256	256	256	382	457.903555	2.737436e+02	6.273	24.93	627.3	627.3	627.3	382	1	0	1	1	1	1	1	382	10.272251	7.297158	1	3	10	17	23	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	221	0.080979	0.057160	0.003100	0.008325	0.1020	0.137200	0.152799	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	221	0.467424	0.250893	0.005448	0.245040	0.588200	0.694700	0.736368	221	106.493791	65.297835	3.158914	3.393953	148.042018	154.4400	169.07000	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	115	0.997557	0.014931	0.9700	0.989500	0.99630	1.003450	1.1144	221	49.466770	37.585199	0.194600	0.209336	64.00	86.100	95.850586	221	79.589887	260.143149	0.469	0.71400	0.9080	79.574276	3154.671796	230	2.624578	7.528053	0	0.13925	0.2515	0.40500	49.282	221	9167449.193850	9.491071e+07	1.200000	43.871980	135.700000	213.327813	1.000000e+09	230	1.142068e+17	1.246946e+18	1.2	98.625	164.80	9.007105e+09	1.799856e+19	382	398.588280	858.693793	25.38	27.5575	28.709316	30.190000	2385.236600	221	0.411794	0.253658	0.047500	0.061234	0.50390	0.638600	0.674016	221	0.226980	1.152211	-11.161023	-0.250368	0.719279	0.813784	0.83522	229	3.567118	3.464055	0.28	2.0100	2.44	3.340	29.48	221	8.097910	5.715975	0.3100	0.832500	10.20	13.7200	15.2799	221	46.742353	25.089262	0.5448	24.504000	58.82000	69.4700	73.6368	221	0.226980	1.152211	-11.161023	-0.250368	0.719279	0.813784	0.83522	221	20.427662	5.139701	12.671135	13.550497	23.599875	24.619799	26.952017	230	2.624578	7.528053	0	0.13925	0.2515	0.40500	49.282	230	16.463979	47.223478	0	0.873515	1.577659	2.540565	309.145986	221	9167449.193850	9.491071e+07	1.200000	43.871980	135.700000	213.327813	1.000000e+09	221	5.750656e+07	5.953750e+08	7.527600	89.703900	275.208931	870.692400	6.273000e+09	58	1	0	1	1	1	1	1
2	2	372	257	0	257	257	257	257	257	372	620.822903	6.220980e+01	24.930	627.30	627.3	627.3	627.3	372	1	0	1	1	1	1	1	372	12.013441	6.633845	1	6	12	18	23	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.130514	0.008845	0.106000	0.124200	0.1313	0.137200	0.150700	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.671086	0.031234	0.586500	0.646000	0.671150	0.697300	0.727200	184	151.834076	4.386037	142.630000	148.780000	151.065000	154.3675	166.98000	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.993584	0.006135	0.9778	0.990025	0.99440	0.998100	1.0078	184	81.869565	5.549159	66.500000	77.925000	82.40	86.025	94.500000	184	0.692750	0.092551	0.533	0.62400	0.6895	0.737000	1.066000	372	2766.176344	39492.151283	0	0.20875	0.2655	0.31850	675922.051	184	21739306.567935	1.462284e+08	59.100000	119.200000	144.800000	162.125000	1.000000e+09	372	2.944173e+15	4.447956e+16	58.8	144.775	1036.00	1.788180e+09	8.178432e+17	372	28.103629	1.128068	25.53	27.2175	27.895000	29.092500	30.550000	184	0.617624	0.031295	0.534100	0.587850	0.62465	0.644950	0.668100	184	0.803138	0.022531	0.739000	0.780050	0.811750	0.821925	0.83590	372	2.391022	0.413320	1.65	2.0900	2.30	2.640	4.14	184	13.051359	0.884499	10.6000	12.420000	13.13	13.7200	15.0700	184	67.108587	3.123379	58.6500	64.600000	67.11500	69.7300	72.7200	184	0.803138	0.022531	0.739000	0.780050	0.811750	0.821925	0.83590	184	24.204380	0.699193	22.737127	23.717520	24.081779	24.608242	26.618843	372	2766.176344	39492.151283	0	0.20875	0.2655	0.31850	675922.051	372	691.385369	9845.268533	0	1.304784	1.662345	1.990109	168507.367314	184	21739306.567935	1.462284e+08	59.100000	119.200000	144.800000	162.125000	1.000000e+09	184	1.363707e+08	9.172907e+08	370.734300	747.741600	908.330400	1017.010125	6.273000e+09	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	3	368	258	0	258	258	258	258	258	368	627.300000	3.870615e-12	627.300	627.30	627.3	627.3	627.3	368	1	0	1	1	1	1	1	368	12.000000	6.642281	1	6	12	18	23	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.129047	0.013166	0.034600	0.124175	0.1329	0.136600	0.147900	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.663425	0.054516	0.244100	0.648700	0.683600	0.692850	0.720600	184	151.082935	2.660760	144.400000	149.417500	151.010000	152.5125	159.68000	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	184	0.994661	0.007113	0.9701	0.990575	0.99470	0.998875	1.0270	184	80.950000	8.261822	21.700000	77.900000	83.35	85.650	92.800000	184	0.710957	0.161196	0.018	0.62475	0.6570	0.727750	1.626000	368	0.277217	0.145476	0	0.19300	0.2880	0.37725	0.612	184	21739290.682065	1.462284e+08	0.000000	123.225000	144.050000	161.650000	1.000000e+09	368	4.431999e+16	6.287283e+17	0.0	140.625	199.65	3.233291e+09	1.164490e+19	368	28.183288	1.182675	25.39	27.2975	28.045000	29.165000	30.520000	184	0.616878	0.037793	0.290200	0.604175	0.62660	0.637225	0.662800	184	0.805804	0.022716	0.587500	0.801125	0.810350	0.816875	0.82980	368	135.884022	2556.217442	1.81	2.0700	2.30	2.645	49039.38	184	12.904674	1.316588	3.4600	12.417500	13.29	13.6600	14.7900	184	66.342500	5.451609	24.4100	64.870000	68.36000	69.2850	72.0600	184	0.805804	0.022716	0.587500	0.801125	0.810350	0.816875	0.82980	184	24.084638	0.424161	23.019289	23.819146	24.073011	24.312530	25.455125	368	0.277217	0.145476	0	0.19300	0.2880	0.37725	0.612	368	1.738985	0.912572	0	1.210689	1.806624	2.366489	3.839076	184	21739290.682065	1.462284e+08	0.000000	123.225000	144.050000	161.650000	1.000000e+09	184	1.363706e+08	9.172907e+08	0.000000	772.990425	903.625650	1014.030450	6.273000e+09	6	1	0	1	1	1	1	1
4	4	460	259	0	259	259	259	259	259	460	503.094600	2.486813e+02	6.273	627.30	627.3	627.3	627.3	460	1	0	1	1	1	1	1	460	12.000000	6.640471	1	6	12	18	23	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	230	0.117905	0.024860	0.006454	0.113695	0.1288	0.134050	0.147600	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	230	0.639222	0.074949	0.250985	0.624622	0.668022	0.686425	0.720500	230	148.810972	5.505934	124.756396	146.769122	149.785000	152.2175	161.85488	0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	138	0.994491	0.006548	0.9810	0.990000	0.99465	0.999975	1.0135	230	73.959233	15.594477	4.048477	71.350102	80.80	84.075	92.600000	230	0.873910	0.265923	0.524	0.67800	0.7695	1.006978	2.331050	276	0.343612	0.223784	0	0.20775	0.3070	0.47300	1.062	230	21739339.795592	1.461485e+08	1.018428	64.496746	123.887334	154.025000	1.000000e+09	276	7.061593e+15	5.620285e+16	31.8	132.100	215.20	2.658501e+10	6.054881e+17	460	28.563831	1.113975	25.41	27.9100	28.292868	29.459491	30.954049	230	0.578576	0.091965	0.070107	0.585761	0.61850	0.633750	0.659900	230	0.765876	0.090430	0.129295	0.773312	0.808300	0.816574	0.82840	276	2.531087	0.756680	1.81	2.0700	2.32	2.735	7.40	230	11.790480	2.486036	0.6454	11.369525	12.88	13.4050	14.7600	230	63.922170	7.494902	25.0985	62.462225	66.80215	68.6425	72.0500	230	0.765876	0.090430	0.129295	0.773312	0.808300	0.816574	0.82840	230	23.722457	0.877719	19.887836	23.396959	23.877730	24.265503	25.801830	276	0.343612	0.223784	0	0.20775	0.3070	0.47300	1.062	276	2.155480	1.403800	0	1.303216	1.925811	2.967129	6.661926	230	21739339.795592	1.461485e+08	1.018428	64.496746	123.887334	154.025000	1.000000e+09	230	1.363709e+08	9.167899e+08	6.388599	404.588088	777.145249	966.198825	6.273000e+09	49	1	0	1	1	1	1	1

ma = Series.rolling(celldf.percentEff, center=False,window=30).mean()
mstd = pd.rolling_std(celldf.percentEff, 30)

plt.figure()


plt.plot(celldf.index, celldf.percentEff, 'k')
plt.plot(ma.index, ma, 'b')
plt.fill_between(mstd.index, ma-2*mstd, ma+2*mstd, color='b', alpha=0.2)
plt.xlim(500,660)
plt.ylim(0,25)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-61-1fc8ea9bca62> in <module>()
----> 1 ma = Series.rolling(celldf.percentEff, center=False,window=30).mean()
      2 mstd = pd.rolling_std(celldf.percentEff, 30)
      3 
      4 plt.figure()
      5 

NameError: name 'Series' is not defined